Aligning Dense Retrievers with LLM Utility via Distillation 事件

Aligning Dense Retrievers with LLM Utility via Distillation arXiv:2604.22722v2 Announce Type: replace-cross Abstract: Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations. Conversely, utility-based approaches leveraging LLM re-ranking often achieve superior performance but are computationally prohibitive and prone to noise inherent in perplexity estimation. We propose Utility-Aligned Embeddings (UA